Rishi Rajalingham

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 55 citations into it
• Cognitive Neuroscience — 39 citations into it; #8,612 of 10,852 by trust here, #3,227 by citations
• Artificial Intelligence — 36 citations into it
• Optimization & Training — 8 citations into it

Whom Rishi Rajalingham trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Du Q. Huynh
3. Geoffrey E. Hinton
4. Kaiming He
5. Ilya Sutskever
6. Christian Szegedy
7. Daniel L. K. Yamins
8. Nicolas Pinto
9. Yoshua Bengio
10. James J. DiCarlo

Who trusts Rishi Rajalingham overall

1. Martin Schrimpf
2. Robert Geirhos
3. Jonas Kubilius
4. Pouya Bashivan
5. Chengxu Zhuang
6. Grace W. Lindsay
7. Kohitij Kar
8. James J. DiCarlo
9. Katherine L. Hermann

Papers from Rishi Rajalingham's perspective

Ordered by how strongly the researchers Rishi Rajalingham trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. ImageNet: A large-scale hierarchical image database
4. ImageNet Large Scale Visual Recognition Challenge
5. Going deeper with convolutions
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks (2018) — 355 citations
• Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs (2019) — 333 citations
• Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? (2018) — 725 citations